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Related Concept Videos

Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

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Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
530
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

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Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
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Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
3.0K
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

729
Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
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Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

278
Additional therapies for treating patients with heart failure (HF) may include procedural interventions, supplemental oxygen, the management of sleep disorders, and nutritional therapy.Procedural InterventionsImplantable Cardioverter-Defibrillator: For patients at risk of life-threatening arrhythmias due to severe left ventricular dysfunction, an Implantable Cardioverter-Defibrillator (ICD) can detect and terminate these arrhythmias, preventing sudden cardiac death and improving survival rates.
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Heart Failure Drugs: Diuretics01:22

Heart Failure Drugs: Diuretics

825
Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
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FuzzyGap: Sequential Pattern Mining for Predicting Chronic Heart Failure in Clinical Pathways.

Eric W Lee1, Joyce C Ho1

  • 1Department of Computer Science, Emory University, Atlanta, GA, United States.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|July 2, 2019
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Summary

This study introduces FuzzyGap, a new method for analyzing electronic health records (EHRs) to find important patient care pathways. FuzzyGap improves on existing techniques by better handling patient data variations for more accurate clinical pathway discovery.

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Area of Science:

  • Health Informatics
  • Data Mining
  • Clinical Decision Support

Background:

  • Electronic health records (EHRs) are growing rapidly, enabling new uses in healthcare.
  • Clinical pathways, sequences of patient diagnoses, are valuable but challenging to extract from EHRs.
  • Existing sequential pattern mining methods struggle with patient data variability (length, visit timing).

Purpose of the Study:

  • To propose FuzzyGap, a novel framework for extracting discriminative clinical pathways from EHRs.
  • To address limitations in existing sequential pattern mining for variable patient data.
  • To emphasize the significance of the last patient visit in pathway analysis.

Main Methods:

  • Utilizing sequential pattern mining techniques.
  • Developing the FuzzyGap framework for enhanced sequence representation.
  • Applying the method to a heart failure case study.

Main Results:

  • FuzzyGap effectively extracts discriminative clinical pathways from EHRs.
  • The framework demonstrates improved pattern extraction despite patient data variations.
  • The case study validates the effectiveness of FuzzyGap in identifying significant patterns.

Conclusions:

  • FuzzyGap offers an effective approach to mining clinical pathways from EHRs.
  • The method enhances the utility of sequential pattern mining in healthcare.
  • This work contributes to better understanding and utilizing patient journey data.